model = cnn() model.summary()

很抱歉，由于我是一个文本 AI，无法直接运行代码。但是我可以帮你解释这段代码的含义。

这段代码是用 Python 编写的，定义了一个名为 `cnn` 的函数，该函数返回一个卷积神经网络模型。`model.summary()` 是用来打印模型的概要信息，包括每层的名称、类型、输出形状和参数数量等。如果你已经定义了 `cnn` 函数并成功运行它，那么调用 `model.summary()` 可以查看该模型的详细结构。

相关问题

def model(self): num_classes = self.config.get("CNN_training_rule", "num_classes") seq_length = self.config.get("CNN_training_rule", "seq_length") conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") model_input = keras.layers.Input((seq_length,1), dtype='float64') # conv1形状[batch_size, seq_length, conv1_num_filters] conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input) conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1) max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2) full_connect = keras.layers.Dense(hidden_dim)(max_poolinged) droped = keras.layers.Dropout(dropout_keep_prob)(full_connect) relued = keras.layers.ReLU()(droped) model_output = keras.layers.Dense(num_classes, activation="softmax")(relued) model = keras.models.Model(inputs=model_input, outputs=model_output) # model.compile(loss="categorical_crossentropy", # optimizer="adam", # metrics=["accuracy"]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) return model给这段代码每行加上注释

def model(self):
    # 获取配置文件中的参数
    num_classes = self.config.get("CNN_training_rule", "num_classes") #分类数
    seq_length = self.config.get("CNN_training_rule", "seq_length") #序列长度
    conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") #第一层卷积核数量
    conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") #第一层卷积核大小
    conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") #第二层卷积核数量
    conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") #第二层卷积核大小
    hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") #全连接层隐藏层大小
    dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") #dropout保留率

    # 定义模型输入
    model_input = keras.layers.Input((seq_length,1), dtype='float64')

    # 第一层卷积
    conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input)

    # 第二层卷积
    conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1)

    # 全局最大池化
    max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2)

    # 全连接层
    full_connect = keras.layers.Dense(hidden_dim)(max_poolinged)

    # dropout层
    droped = keras.layers.Dropout(dropout_keep_prob)(full_connect)

    # relu激活层
    relued = keras.layers.ReLU()(droped)

    # 输出层
    model_output = keras.layers.Dense(num_classes, activation="softmax")(relued)

    # 定义模型
    model = keras.models.Model(inputs=model_input, outputs=model_output)

    # 编译模型
    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])

    # 打印模型结构
    print(model.summary())

    return model

### 网络构建 def cnn_create(): loss = 'sparse_categorical_crossentropy' metrics = ['acc'] inputs = tf.keras.Input(shape=(n1_,n2_)) x = layers.Conv1D(64, 3, activation='relu')(inputs) x = layers.MaxPooling1D(4)(x) # 全局平均池化GAP层 x = layers.GlobalAveragePooling1D()(x) # 几个密集分类层 x = layers.Dense(32, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01))(x) # 退出层 x = layers.Dropout(0.5)(x) outputs = layers.Dense(5, activation='softmax')(x) model = tf.keras.Model(inputs, outputs) model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=loss, metrics=metrics) print("实例化模型成功，参数如下：") print(model.summary()) return model这个网络模型我想把它变小点，该怎么修改，输入的是2400个128*8的特征

要将模型变小，你可以尝试调整以下几个方面：

1. 减少卷积核的数量：在 `layers.Conv1D` 层中，将卷积核的数量从 64 减少到较小的值，例如 32 或 16。这将减少模型中的参数数量。

x = layers.Conv1D(32, 3, activation='relu')(inputs)

2. 减少全连接层的节点数：在 `layers.Dense` 层中，将节点数从 32 减少到较小的值。同样地，这将减少模型中的参数数量。

x = layers.Dense(16, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01))(x)

3. 减少正则化项的强度：你可以尝试减小正则化项的值，例如将 `l2(0.01)` 修改为 `l2(0.001)`，以减少正则化对模型的影响。

x = layers.Dense(16, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)

这些修改都会减小模型的容量，并且可能会影响模型的性能。你可以根据实际情况进行调整和实验，找到适合你任务的最佳模型大小。希望这些建议能对你有所帮助！如果还有其他问题，请随时提问。